Text Generation
Transformers
TensorBoard
Safetensors
gpt2
adventure
travel-itinerary
custom-model
text-generation-inference
Instructions to use yoonusajward01/triptuner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yoonusajward01/triptuner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yoonusajward01/triptuner")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yoonusajward01/triptuner") model = AutoModelForCausalLM.from_pretrained("yoonusajward01/triptuner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use yoonusajward01/triptuner with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yoonusajward01/triptuner" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yoonusajward01/triptuner", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yoonusajward01/triptuner
- SGLang
How to use yoonusajward01/triptuner with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "yoonusajward01/triptuner" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yoonusajward01/triptuner", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "yoonusajward01/triptuner" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yoonusajward01/triptuner", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use yoonusajward01/triptuner with Docker Model Runner:
docker model run hf.co/yoonusajward01/triptuner
Download model.safetensors from yoonusajward01/triptuner: direct link, hf CLI and curl.
- Browser
- Download file 498 MB
-
https://huggingface.co/yoonusajward01/triptuner/resolve/main/model.safetensors
- Command line
-
hf download hf://yoonusajward01/triptuner/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/yoonusajward01/triptuner/resolve/main/model.safetensors
498 MB
- Xet hash:
- 6f0ce1b21e5df3ab4102be214003f6593dcae3feae1e5770e528d4340ec23164
- Size of remote file:
- 498 MB
- SHA256:
- c7d00560d8910fbed77ffad4065dee5011c41ba401b1064e749c498ba9e20373
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.